LOG-01Cloud & AI Related

Logging and Monitoring Policy and Procedures

Specification

Establish, document, approve, communicate, apply, evaluate and maintain policies and procedures for logging and monitoring. Review and update the policies and procedures at least annually, or upon significant changes.

Threat coverage

Model manipulation

Data poisoning

Sensitive data disclosure

Model theft

Model/Service Failure

Insecure supply chain

Insecure apps/plugins

Denial of Service

Loss of governance

Architectural relevance

Physical infrastructure

Network

Compute

Storage

Application

Data

Lifecycle

Preparation

Data collection, Data curation, Data storage, Resource provisioning, Team and expertise

Development

Training

Evaluation

Evaluation, Validation/Red Teaming, Re-evaluation

Deployment

Orchestration, AI Services supply chain, AI applications

Delivery

Operations, Maintenance, Continuous monitoring, Continuous improvement

Retirement

Archiving, Data deletion

Ownership / SSRM

Shared Cloud Service Provider-Model Provider (Shared CSP-MP)

The CSP and MP are jointly responsible and accountable for the design, development, implementation, and enforcement of the control to mitigate security, privacy, or compliance risks associated with Large Language Model (LLM)/GenAI technologies in the context of the services or products they develop and offer.

Model

Owned by the Model Provider (MP)

The model provider (MP) designs, develops, and implements the control as part of their services or products to mitigate security, privacy, or compliance risks associated with the Large Language Model (LLM). Model Providers are entities that develop, train, and distribute foundational and fine-tuned AI models for various applications. They create the underlying AI capabilities that other actors build upon. Model Providers are responsible for model architecture, training methodologies, performance characteristics, and documentation of capabilities and limitations. They operate at the foundation layer of the AI stack and may provide direct API access to their models. Examples: OpenAI (GPT, DALL-E, Whisper), Anthropic(Claude), Google(Gemini), Meta(Llama), as well as any customized model.

Orchestrated

Shared Model Provider-Orchestrated Service Provider (Shared MP-OSP)

The MP and OSP are jointly responsible and accountable for the design, development, implementation, and enforcement of the control to mitigate security, privacy, or compliance risks associated with Large Language Model (LLM)/GenAI technologies in the context of the services or products they develop and offer.

Application

Owned by the Application Provider (AP)

The Application Provider (AP) is responsible for the design, development, implementation, and enforcement of the control to mitigate security, privacy, or compliance risks associated with Large Language Model (LLM)/GenAI technologies in the context of the services or products they develop and offer. The AP is responsible and accountable for the implementation of the control within its own infrastructure/environment. If the control has downstream implications on the users/customers, the AP is responsible for enabling the customer and/or upstream partner in the implementation/configuration of the control within their risk management approach. The AP is accountable for carrying out the due diligence on its upstream providers (e.g MPs, Orchestrated Services) to verify that they implement the control as it relates to the service/product develop and offered by the AP. These providers build and offer end-user applications that leverage generative AI models for specific tasks such as content creation, chatbots, code generation, and enterprise automation. These applications are often delivered as software-as-a-service (SaaS) solutions. These providers focus on user interfaces, application logic, domain-specific functionality, and overall user experience rather than underlying model development. Example: OpenAI (GPTs,Assistants), Zapier, CustomGPT, Microsoft Copilot (integrated into Office products), Jasper (AI-driven content generation), Notion AI (AI-enhanced productivity tools), Adobe Firefly (AI-generated media), and AI-powered customer service solutions like Amazon Rufus, as well as any organization that develops its AI-based application internally.

Implementation guidelines

[All Actors]
1. Establish Logging and Monitoring Policies.

2. Document types of events to log (e.g., access, errors, configuration changes, inference requests, etc.).

3. Define retention periods, log sensitivity classification, access controls, and escalation paths in line with business, regulatory and contractual needs. 

4. Apply and Enforce Policies; make policy documents visible across departments in the entire organization.

5. Implement automated logging across infrastructure, applications, and model endpoints.

6. Ensure all logs are time-synchronized and tamper-resistant across all environments.

7. Get formal approval from compliance and/or security leadership and re-approval whenever material changes occur.

8. Conduct regular reviews and trigger reviews upon: changes in architecture or supply chain, emergence of new threats or vulnerabilities, changes in regulations or threat intel.

Auditing guidelines

1. Conduct interviews with personnel responsible for documenting, maintaining, and communicating organizational logging and monitoring policies, procedures, and standards (the Policies).

2. Inspecting Records and Documents: Obtain and review the Policies to ensure they are adequate for the organization to manage risks associated with logging and monitoring. Verify that the Policies define the personnel or roles responsible for their dissemination, identify an official accountable for managing the Policies, specify the frequency of reviews and updates (annually), and outline events that necessitate policy updates. Review the Policies by performing the following verification steps:

2.1 Verify that logging and monitoring infrastructure services (e.g., Cloud Logging, CloudWatch) comply with CSP's internal logging policies.

2.2 Confirm that the CSP enforces logging for IAM events, data access, and service configuration changes.

2.3 Ensure logs are protected using encryption at rest and in transit, with strict access controls.

2.4 Check for evidence of monitoring policies applied to CSP-managed services used by tenants.

2.5 Validate that CSP provides customers with access to their relevant logs or offers secure export functionality.

2.6 Confirm that CSP logs are subject to regular audits and support legal or compliance inquiries.

2.7 Ensure that logs support investigation of cross-tenant issues and supply chain-related anomalies.

2.8 Verify controls are in place to prevent and detect disabling or tampering with log pipelines.

2.9 Examine policy and procedures for adequacy, approval, communication, and effectiveness as applicable to planning, delivery and support of the organization's logging and monitoring requirements.

2.10 Examine policy and procedures for evidence of review at least annually.

3. Verify that the Policies are communicated, reviewed and updated at least annually or upon significant changes, are approved, and communicated to relevant stakeholders.

Standards mappings

ISO 42001Partial Gap

ISO 42001 A.2.2
ISO 42001 A.2.4
ISO 27001 A.5.1

Addendum

ISO 42001 nor ISO 27001 speak to "approve" for policies.

EU AI ActPartial Gap

Article 17 (1)

Addendum

Explicitly set the requirement of formalizing (and hence periodically reviewing) policies and procedures of logging and monitoring.

NIST AI 600-1No Gap

GV-4.1-001
MG-4.1-002
MG-4.3-002
MG-3.2-006

Addendum

N/A

BSI AIC4No Gap

C4 PC-02
C5 OPS-10

Addendum

N/A

AI-CAIQ questions (2)

LOG-01.1

Are logging and monitoring policies and procedures established, documented, approved, communicated, applied, evaluated, and maintained?

LOG-01.2

Are policies and procedures reviewed, approved and updated at least annually, or upon significant changes?